Readit NewsFor example, if I wanted an AI to help me highlight to the user where in a body of text I mentioned AI, I might have it return something like:
<text>Great writeup. To add an example, I personally use JSON for most of my work, but have found myself using XML for certain <ai-mention>AI</ai-mention> use cases that require annotating an original text with segments.</text>
SelectIQ works with clinical trial sites and their referring physicians to automate the patient referral process, massively speeding up enrollment.
Bringing new drugs to market is primarily rate limited on the speed of clinical trials, which are themselves bottlenecked by patient recruitment. 80% of clinical trials don’t meet their recruitment timeline, and being behind on recruitment can cost pharma companies millions of dollars per day.
We're looking to bring on our first engineering hire to help us build out the platform and build an amazing culture. More details and application can be found here: https://www.ycombinator.com/companies/selectiq/jobs/4aRBMcX-...
Feel free to email me directly - jason@selectiq.ai
The shared feature between Temporal and those three is the workflow orchestration piece. All 3 can manage a dependency graph of jobs, handle retries, start from checkpoints, etc.
At a high level the big reason they’re different is Temporal is entirely focused on the orchestration piece, and the others are much more focused on the data piece, which comes out in a lot of the different features. Temporal has SDKs in most languages, and has a queuing system that allows you to run different workflows or even activities (tasks within a workflow) in different workers, manage concurrency, etc. You can write a parent workflow that orchestrates sub-workflows that could live in 5 other services. It’s just really composable and fits much more nicely into the critical path of your app.
Prefect is probably the closest of your list to temporal, in that it’s less opinionated than others about the workflows being “data oriented”, but it’s still only in python, and it deosn't have queueing. In short this means that your workflows are kinda supposed to run in one box running python somewhere. Temporal will let you define a 10 part workflow where two parts run on a python service running with a GPU, and the remaining parts are running in the same node.js process as your main server.
Dagster’s feature set is even more focused on data-workflows, as your workflows are meant to produce data “assets” which can be materialized/cached, etc.
They’re pretty much all designed for a data engineering team to manage many individual pipelines that are external from your application code, whereas temporal is designed to be a system that manages workflow complexity for code that (more often) runs in your application.
EDIT: I doubt OP is reading this but I would highly recommend they get insurance before giving birth...sure, they won't cover your at home birth, but they will cover your hospital stay if you end up having potentially fatal complications
but we were all young once, I remember thinking the only thing holding me back was 4.77MHz